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A Neuro-Symbolic Reasoning Engine for Compilation-Free Patch Presence Testing

Siyuan Li, Jiayi Zhou, Zhiyuan Xia, Weijie Wang, Yaowen Zheng, Weiran Tang, Hong Li, Hongsong Zhu, Shanqing Guo · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27871 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

このコードは、現代のソフトウェアエコシステムで公式に再利用されるため、1日間の脆弱性の拡散を容易にし、重大なセキュリティリスクを引き起こします。パッチが存在するかどうかを決定することは重要ですが、伝統的な方法は、しばしば脆弱なコンパイルプロセスに依存して、比較的有効性と正確性に苦しんでいます。私たちの以前の仕事では、Laresを導入し、パッチ存在テストのための最初のコンパイルフリーフレームワークは、最終的な決定のためのニューラルチャンネル(LLMs)を組み合わせます。しかし、LaresはLLMとSMTソルバーを2つの別々の並行決定チャンネルとして扱います:シンボルチャンネル(Z3)は、コードケースのわずか

Machine translation (Meta M2M100); the English original follows.

Abstract

Code reuse in modern software ecosystems facilitates the spread of 1-day vulnerabilities, posing significant security risks. Determining if patches are present is crucial, but traditional methods suffer from limited usability and accuracy, often depending on a fragile compilation process. In our prior work, we introduced Lares, the first compilation-free framework for patch presence testing that combines large language models (LLMs) with SMT solvers. However, Lares treats the LLM and the SMT solver as two separate, parallel decision channels: the symbolic channel (Z3) succeeded in only 20% of code cases, and the remaining cases fell back to the neural channel (the LLM) for the final decision. Letting the LLM act as the final arbiter turns verification into an informal, non-deterministic process that is prone to hallucination errors, while the two channels never reinforce each other. To overcome these limitations, we propose SyLares, which reformulates compilation-free patch presence testing as neuro-symbolic reasoning. Instead of letting the LLM emit a final verdict, SyLares uses the LLM as a neural feature extractor that perceives heterogeneous source code and decompiled pseudocode and produces specification-compliant SMT-LIB inputs, and delegates the final, deterministic judgment to the symbolic Z3 solver. This neural-perceives, symbolic-decides coupling is realized by a Code2SMT reasoning engine that combines deterministic syntactic parsing with LLM-driven code normalization and symbol alignment, so that every case is reduced to a formal equivalence query rather than an opaque LLM guess.

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Topics: Computer science, Programming language, Symbolic execution, Reuse, Nondeterministic algorithm, Concolic testing

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